Particle analyzers are used to analyze biological and industrial samples to determine the count and/or distribution of one or more types of particles contained in the samples. In the medical industry, in order to analyze or research bodily fluids, particle analyzers including hematology analyzers and flow cytometers can be used. For example, hematology analyzers and flow cytometers are used to measure and differentiate between different types of blood cells by capturing and analyzing signals. The signals are produced by using probes interacting with the sample as blood cells pass through a small aperture or measurement region. In general, a sample of blood is diluted in a liquid before being directed to flow through a flow cell that contains the measurement region. One or more sensors or detectors are arranged to detect various characteristics of the blood cells that pass through the measurement region as the blood cells interact with the probe.
During the measurement, the diluted blood sample is injected into the flow cell at a substantially constant rate. A reference reading is obtained when no blood cell is in the measurement region of the flow cell. When a blood cell is present, the physical properties of the measurement region are altered. Therefore, the signal differs from the respective reference signal when a cell is in the measurement region. The deviation of sensor readings gradually increases as the blood cell flows towards the midpoint of the measurement region and then gradually decreases as the blood cell flows away from the midpoint.
It is common practice to collect and analyze a maximum signal (i.e., peak) caused by the interaction of the blood cell and one or more measurement parameters, for example, direct current (DC) which obeys the Coulter Principle, Radio Frequency (RF), Light Scatter (LS), Axial Light Loss (ALL), ultrasound, etc. In general, the peak of the signal is a well-defined function of the interaction between a type of blood cell and measurement parameter, i.e., type of stimulus and sensor. For example, the peak of a signal generated by a DC measurement indicates the volume of the cell, and the cell may be categorized based on the volume. Cells are counted and cell types are identified based on one or more measurement parameters.
Applications based on the above relationship rely on blood cells passing through the measurement region one at a time. If multiple blood cells pass through the measurement region simultaneously (i.e., coincidence) the maximum deviation between the captured signal and the reference signal is no longer a well-defined function of the interaction between a type of blood cell and measurement parameters. Moreover, in the presence of severe coincidence, histograms accumulated from received signals can be distorted and analytic results may be compromised.
Conventional approaches to address coincidence include the application of statistical methods to particle data counts and histograms to compensate for the expected coincidence errors. Another method to detect coincidence is based on the area and the peak of a signal generated when a particle passes through a measurement region. However, statistical methods may not yield accurate coincidence elimination due to inherent estimation errors. Also, methods relying on a ratio of area to peak of a signal generated by a particle may not be sufficiently accurate for particles of varied sizes and shapes. Area-to-peak-based measures may also yield inconsistent results when particles pass through the measurement region at various orientations.
Therefore, what are needed are improved methods and systems to identify and compensate for data representative of coincidence in particle analyzer data.